Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution
- 1 September 2022
- journal article
- research article
- Published by Elsevier BV in Environmental Research
- Vol. 212 (Pt B), 113322
- https://doi.org/10.1016/j.envres.2022.113322
Abstract
No abstract availableThis publication has 95 references indexed in Scilit:
- Supermicron modes of ammonium ions related to fog in rural atmosphereAtmospheric Chemistry and Physics, 2012
- Application of positive matrix factorization in characterization of PM10 and PM2.5 emission sources at urban roadsideChemosphere, 2012
- Formation of secondary organic carbon and cloud impact on carbonaceous aerosols at Mount Tai, North ChinaAtmospheric Environment, 2012
- Atmospheric ammonia and particulate ammonium from agricultural sources in the North China PlainAtmospheric Environment, 2011
- Advanced factor analysis for multiple time resolution aerosol composition dataAtmospheric Environment, 2004
- Real-time measurements of ammonia, acidic trace gases and water-soluble inorganic aerosol species at a rural site in the Amazon BasinAtmospheric Chemistry and Physics, 2004
- Evaluating the first‐order effect of intraannual temperature variability on urban air pollutionPublished by American Geophysical Union (AGU) ,2003
- Sources of fine particle composition in the northeastern USAtmospheric Environment, 2001
- Greedy function approximation: A gradient boosting machine.The Annals of Statistics, 2001
- Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data valuesEnvironmetrics, 1994